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DOC: update the pandas.Index.is_categorical docstring #20167

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Merged
merged 7 commits into from
Mar 12, 2018
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DOC: update the pandas.Index.is_categorical docstring #20167

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354795e
pandas.Index.is_categorical from base.py documented
mariadelmarbibiloni Mar 10, 2018
1755bee
pandas.Index.is_categorical from base.py documented and some errors s…
mariadelmarbibiloni Mar 10, 2018
5a972b4
pandas.Index.is_categorical from base.py with new examples and extend…
mariadelmarbibiloni Mar 11, 2018
e6e12c6
Added See Also.
TomAugspurger Mar 11, 2018
a4586f2
Added back whitepace
TomAugspurger Mar 11, 2018
218737a
to_frame formatting
TomAugspurger Mar 12, 2018
1461f4a
Merge remote-tracking branch 'upstream/master' into mariadelmarbibilo…
TomAugspurger Mar 12, 2018
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37 changes: 36 additions & 1 deletion pandas/core/indexes/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1156,7 +1156,7 @@ def to_frame(self, index=True):
>>> idx = pd.Index(['Ant', 'Bear', 'Cow'], name='animal')
>>> idx.to_frame()
animal
animal
animal
Ant Ant
Bear Bear
Cow Cow
Expand Down Expand Up @@ -1471,6 +1471,41 @@ def is_object(self):
return is_object_dtype(self.dtype)

def is_categorical(self):
"""
Check if the Index type is categorical.

The given object must be a pandas DataFrame or pandas Series.
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this only applies to a Series or an Index, IOW it must be 1D

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Thanks @jreback ! I have changed the exended summary. Is it better now?


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I agree! Thank you :)

Returns
-------
boolean
True if index is categorical.

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Thanks a lot for the See Also!

Examples
--------
>>> s = pd.Series(["Watermelon", "Orange", "Apple",
... "Watermelon"]).astype('category')
>>> df = pd.DataFrame({'Weight (grams)': [2000, 230, 160, 1300]},
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don't create a DataFrame here just to use its index, just use the series above

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Ok, Thank you! I have changed the examples.

... index=s)
>>> df
Weight (grams)
Watermelon 2000
Orange 230
Apple 160
Watermelon 1300
>>> df.index.is_categorical()
True

>>> s = pd.Series(["Peter", "Adam", "Elisabeth", "Margareth"])
>>> s
0 Peter
1 Adam
2 Elisabeth
3 Margareth
dtype: object
>>> s.index.is_categorical()
False
"""
return self.inferred_type in ['categorical']

def is_interval(self):
Expand Down